OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Document VQA Models via Retrieval-Augmented Generation
Venue: arXiv cs.CV 2025
Year: 2025

Document Visual Question Answering (Document VQA) must cope with documents that span dozens of pages, yet leading systems still concatenate every page or rely on very large vision-language models, both of which are memory-hungry. Retrieval-Augmented Generation (RAG) offers an attractive alternative, first retrieving a concise set of relevant segments before generating answers from this selected evidence. In this paper, we systematically evaluate the impact of incorporating RAG into Document VQA through different retrieval variants - text-based retrieval using OCR tokens and purely visual retrieval without OCR - across multiple models and benchmarks. Evaluated on the multi-page datasets MP-DocVQA, DUDE, and InfographicVQA, the text-centric variant improves the "concatenate-all-pages" baseline by up to +22.5 ANLS, while the visual variant achieves +5.0 ANLS improvement without requiring any text extraction. An ablation confirms that retrieval and reranking components drive most of the gain, whereas the layout-guided chunking strategy - proposed in several recent works to leverage page structure - fails to help on these datasets. Our experiments demonstrate that careful evidence selection consistently boosts accuracy across multiple model sizes and multi-page benchmarks, underscoring its practical value for real-world Document VQA.
Learning with springs and sticks
Venue: arXiv cs.LG 2025
Year: 2025

Learning is a physical process. Here, we aim to study a simple dynamical system composed of springs and sticks capable of arbitrarily approximating any continuous function. The main idea of our work is to use the sticks to mimic a piecewise-linear approximation of the given function, use the potential energy of springs to encode a desired mean squared error loss function, and converge to a minimum-energy configuration via dissipation. We apply the proposed simulation system to regression tasks and show that its performance is comparable to that of multi-layer perceptrons. In addition, we study the thermodynamic properties of the system and find a relation between the free energy change of the system and its ability to learn an underlying data distribution. We empirically find a \emph{thermodynamic learning barrier} for the system caused by the fluctuations of the environment, whereby the system cannot learn if its change in free energy hits such a barrier. We believe this simple model can help us better understand learning systems from a physical point of view.
Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) have emerged as the leading paradigm for learning over graph-structured data. However, their performance is limited by issues inherent to graph topology, most notably oversquashing and oversmoothing. Recent advances in graph rewiring aim to mitigate these limitations by modifying the graph topology to promote more effective information propagation. In this work, we introduce TRIGON, a novel framework that constructs enriched, non-planar triangulations by learning to select relevant triangles from multiple graph views. By jointly optimizing triangle selection and downstream classification performance, our method produces a rewired graph with markedly improved structural properties such as reduced diameter, increased spectral gap, and lower effective resistance compared to existing rewiring methods. Empirical results demonstrate that TRIGON outperforms state-of-the-art approaches on node classification tasks across a range of homophilic and heterophilic benchmarks.
Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices
Venue: arXiv cs.DC 2025
Year: 2025

Federated fine-tuning of Mixture-of-Experts (MoE)-based large language models (LLMs) is challenging due to their massive computational requirements and the resource constraints of participants. Existing working attempts to fill this gap through model quantization, computation offloading, or expert pruning. However, they cannot achieve desired performance due to impractical system assumptions and a lack of consideration for MoE-specific characteristics. In this paper, we propose FLUX, a system designed to enable federated fine-tuning of MoE-based LLMs across participants with constrained computing resources (e.g., consumer-grade GPUs), aiming to minimize time-to-accuracy. FLUX introduces three key innovations: (1) quantization-based local profiling to estimate expert activation with minimal overhead, (2) adaptive layer-aware expert merging to reduce resource consumption while preserving accuracy, and (3) dynamic expert role assignment using an exploration-exploitation strategy to balance tuning and non-tuning experts. Extensive experiments on LLaMA-MoE and DeepSeek-MoE with multiple benchmark datasets demonstrate that FLUX significantly outperforms existing methods, achieving up to 4.75X speedup in time-to-accuracy.
A Theory of Goal-Oriented Medium Access: Protocol Design and Distributed Bandit Learning
Venue: arXiv cs.NI 2025
Year: 2025

The Goal-oriented Communication (GoC) paradigm breaks the separation between communication and the content of the data, tailoring communication decisions to the specific needs of the receiver and targeting application performance. While recent studies show impressive encoding performance in point-to-point scenarios, the multi-node distributed scenario is still almost unexplored. Moreover, the few studies to investigate this consider a centralized collision-free approach, where a central scheduler decides the transmission order of the nodes. In this work, we address the Goal-oriented Multiple Access (GoMA) problem, in which multiple intelligent agents must coordinate to share a wireless channel and avoid mutual interference. We propose a theoretical framework for the analysis and optimization of distributed GoMA, serving as a first step towards its complete characterization. We prove that the problem is non-convex and may admit multiple Nash Equilibrium (NE) solutions. We provide a characterization of each node's best response to others' strategies and propose an optimization approach that provably reaches one such NE, outperforming centralized approaches by up to 100% while also reducing energy consumption. We also design a distributed learning algorithm that operates with limited feedback and no prior knowledge.
AutoRing: Imitation Learning--based Autonomous Intraocular Foreign Body Removal Manipulation with Eye Surgical Robot
Venue: arXiv cs.RO 2025
Year: 2025

Intraocular foreign body removal demands millimeter-level precision in confined intraocular spaces, yet existing robotic systems predominantly rely on manual teleoperation with steep learning curves. To address the challenges of autonomous manipulation (particularly kinematic uncertainties from variable motion scaling and variation of the Remote Center of Motion (RCM) point), we propose AutoRing, an imitation learning framework for autonomous intraocular foreign body ring manipulation. Our approach integrates dynamic RCM calibration to resolve coordinate-system inconsistencies caused by intraocular instrument variation and introduces the RCM-ACT architecture, which combines action-chunking transformers with real-time kinematic realignment. Trained solely on stereo visual data and instrument kinematics from expert demonstrations in a biomimetic eye model, AutoRing successfully completes ring grasping and positioning tasks without explicit depth sensing. Experimental validation demonstrates end-to-end autonomy under uncalibrated microscopy conditions. The results provide a viable framework for developing intelligent eye-surgical systems capable of complex intraocular procedures.
Planning-Query-Guided Model Generation for Model-Based Deformable Object Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Efficient planning in high-dimensional spaces, such as those involving deformable objects, requires computationally tractable yet sufficiently expressive dynamics models. This paper introduces a method that automatically generates task-specific, spatially adaptive dynamics models by learning which regions of the object require high-resolution modeling to achieve good task performance for a given planning query. Task performance depends on the complex interplay between the dynamics model, world dynamics, control, and task requirements. Our proposed diffusion-based model generator predicts per-region model resolutions based on start and goal pointclouds that define the planning query. To efficiently collect the data for learning this mapping, a two-stage process optimizes resolution using predictive dynamics as a prior before directly optimizing using closed-loop performance. On a tree-manipulation task, our method doubles planning speed with only a small decrease in task performance over using a full-resolution model. This approach informs a path towards using previous planning and control data to generate computationally efficient yet sufficiently expressive dynamics models for new tasks.
Optimizing Highway Traffic Flow in Mixed Autonomy: A Multiagent Truncated Rollout Approach
Venue: arXiv cs.MA 2025
Year: 2025

The development of connected and autonomous vehicles (CAVs) offers substantial opportunities to enhance traffic efficiency. However, in mixed autonomy environments where CAVs coexist with human-driven vehicles (HDVs), achieving efficient coordination among CAVs remains challenging due to heterogeneous driving behaviors. To address this, this paper proposes a multiagent truncated rollout approach that enhances CAV speed coordination to improve highway throughput while reducing computational overhead. In this approach, a traffic density evolution equation is formulated that comprehensively accounts for the presence or absence of CAVs, and a distributed coordination control framework is established accordingly. By incorporating kinematic information from neighbor agents and employing an agent-by-agent sequential solution mechanism, our method enables explicit cooperation among CAVs. Furthermore, we introduce a truncated rollout scheme that adaptively shortens the optimization horizon based on the evaluation of control sequences. This significantly reduces the time complexity, thereby improving real-time performance and scalability. Theoretical analysis provides rigorous guarantees on the stability and performance improvement of the system. Simulations conducted on real-world bottleneck scenarios demonstrate that, in large-scale mixed traffic flows, the proposed method outperforms conventional model predictive control methods by reducing both the average travel time in the bottleneck area and overall computational time, highlighting its strong potential for practical deployment.
Beyond Competitive Gaming: How Casual Players Evaluate and Respond to Teammate Performance
Venue: arXiv cs.HC 2025
Year: 2025

Teammate performance evaluation fundamentally shapes intervention design in video games. However, our current understanding stems primarily from competitive E-Sports contexts where individual performance directly impacts outcomes. This research addresses whether performance evaluation mechanisms and behavioural responses identified in competitive games generalize to casual cooperative games. We investigated how casual players evaluate teammate competence and respond behaviourally in a controlled between-subjects experiment (N=23). We manipulated confederate performance in Overcooked 2, combining observations, NASA TLX self-reports, and interviews. We present two key findings. (1) Observations revealed frustration behaviours completely absent in self-report data. Thus, these instruments assess fundamentally distinct constructs. (2) Participants consistently evaluated teammate performance through relative comparison rather than absolute metrics. This contradicts task-performance operationalizations dominant in competitive gaming research. Hence, performance evaluation frameworks from competitive contexts cannot be directly applied to casual cooperative games. We provide empirical evidence that performance evaluation in casual games requires a comparative operationalization.
Automated Feature Tracking for Real-Time Kinematic Analysis and Shape Estimation of Carbon Nanotube Growth
Venue: arXiv cs.CV 2025
Year: 2025

Carbon nanotubes (CNTs) are critical building blocks in nanotechnology, yet the characterization of their dynamic growth is limited by the experimental challenges in nanoscale motion measurement using scanning electron microscopy (SEM) imaging. Existing ex situ methods offer only static analysis, while in situ techniques often require manual initialization and lack continuous per-particle trajectory decomposition. We present Visual Feature Tracking (VFTrack) an in-situ real-time particle tracking framework that automatically detects and tracks individual CNT particles in SEM image sequences. VFTrack integrates handcrafted or deep feature detectors and matchers within a particle tracking framework to enable kinematic analysis of CNT micropillar growth. A systematic using 13,540 manually annotated trajectories identifies the ALIKED detector with LightGlue matcher as an optimal combination (F1-score of 0.78, $α$-score of 0.89). VFTrack motion vectors decomposed into axial growth, lateral drift, and oscillations, facilitate the calculation of heterogeneous regional growth rates and the reconstruction of evolving CNT pillar morphologies. This work enables advancement in automated nano-material characterization, bridging the gap between physics-based models and experimental observation to enable real-time optimization of CNT synthesis.
VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space
Venue: arXiv cs.CV 2025
Year: 2025

3D local editing of specified regions is crucial for game industry and robot interaction. Recent methods typically edit rendered multi-view images and then reconstruct 3D models, but they face challenges in precisely preserving unedited regions and overall coherence. Inspired by structured 3D generative models, we propose VoxHammer, a novel training-free approach that performs precise and coherent editing in 3D latent space. Given a 3D model, VoxHammer first predicts its inversion trajectory and obtains its inverted latents and key-value tokens at each timestep. Subsequently, in the denoising and editing phase, we replace the denoising features of preserved regions with the corresponding inverted latents and cached key-value tokens. By retaining these contextual features, this approach ensures consistent reconstruction of preserved areas and coherent integration of edited parts. To evaluate the consistency of preserved regions, we constructed Edit3D-Bench, a human-annotated dataset comprising hundreds of samples, each with carefully labeled 3D editing regions. Experiments demonstrate that VoxHammer significantly outperforms existing methods in terms of both 3D consistency of preserved regions and overall quality. Our method holds promise for synthesizing high-quality edited paired data, thereby laying the data foundation for in-context 3D generation. See our project page at https://huanngzh.github.io/VoxHammer-Page/.
Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models
Venue: arXiv cs.LG 2025
Year: 2025

We present a new efficient hybrid parameter estimation method based on the idea, that if nonlinear dynamic models are stated in terms of a system of equations that is linear in terms of the parameters, then regularized ordinary least squares can be used to estimate these parameters from time series data. We introduce the term "Physics-Informed Regression" (PIR) to describe the proposed data-driven hybrid technique as a way to bridge theory and data by use of ordinary least squares to efficiently perform parameter estimation of the model coefficients of different parameter-linear models; providing examples of models based on nonlinear ordinary equations (ODE) and partial differential equations (PDE). The focus is on parameter estimation on a selection of ODE and PDE models, each illustrating performance in different model characteristics. For two relevant epidemic models of different complexity and number of parameters, PIR is tested and compared against the related technique, physics-informed neural networks (PINN), both on synthetic data generated from known target parameters and on real public Danish time series data collected during the COVID-19 pandemic in Denmark. Both methods were able to estimate the target parameters, while PIR showed to perform noticeably better, especially on a compartment model with higher complexity. Given the difference in computational speed, it is concluded that the PIR method is superior to PINN for the models considered. It is also demonstrated how PIR can be applied to estimate the time-varying parameters of a compartment model that is fitted using real Danish data from the COVID-19 pandemic obtained during a period from 2020 to 2021. The study shows how data-driven and physics-informed techniques may support reliable and fast -- possibly real-time -- parameter estimation in parameter-linear nonlinear dynamic models.
Real-Time Intuitive AI Drawing System for Collaboration: Enhancing Human Creativity through Formal and Contextual Intent Integration
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a real-time generative drawing system that interprets and integrates both formal intent - the structural, compositional, and stylistic attributes of a sketch - and contextual intent - the semantic and thematic meaning inferred from its visual content - into a unified transformation process. Unlike conventional text-prompt-based generative systems, which primarily capture high-level contextual descriptions, our approach simultaneously analyzes ground-level intuitive geometric features such as line trajectories, proportions, and spatial arrangement, and high-level semantic cues extracted via vision-language models. These dual intent signals are jointly conditioned in a multi-stage generation pipeline that combines contour-preserving structural control with style- and content-aware image synthesis. Implemented with a touchscreen-based interface and distributed inference architecture, the system achieves low-latency, two-stage transformation while supporting multi-user collaboration on shared canvases. The resulting platform enables participants, regardless of artistic expertise, to engage in synchronous, co-authored visual creation, redefining human-AI interaction as a process of co-creation and mutual enhancement.
MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts
Venue: arXiv cs.CL 2025
Year: 2025

Despite LLMs' excellent code creation capabilities, multilingual code generation remains extremely challenging. To address this, we intent to improve the multi-programming-lingual (MultiPL) performance of the base LLMs while retaining the most popular ones using restricted computational resources. We consider MultiPL to be a special case of multiple natural languages and propose a MultiPL extension of LLMs utilizing a hybrid mixture of experts (MoE), called MultiPL-MoE. Specifically, MultiPL-MoE combines two paired MoEs to optimize expert selection at both the token and segment levels. The token-level MoE is a standard upcycling MoE structure with a shared expert and a novel gate weight normalization approach that aids in the final fusion with the segment-level MoE. The segment-level MoE incorporates two innovative designs to better capture the syntactic structure and contextual patterns of programming languages: First, using a sliding window to partition the input token sequence into multiple segments; Then, adopting an expert-choice routing strategy that allows experts to select the top-k segments. The results of the experiment proved the effectiveness of MultiPL-MoE.
Rethinking Denial-of-Service: A Conditional Taxonomy Unifying Availability and Sustainability Threats
Venue: arXiv cs.CR 2025
Year: 2025

This paper proposes a unified, condition-based framework for classifying both legacy and cloud-era denial-of-service (DoS) attacks. The framework comprises three interrelated models: a formal conditional tree taxonomy, a hierarchical lattice structure based on order theory, and a conceptual Venn diagram. At its core, the taxonomy introduces six observable conditions (C0-C5) grounded in real-world attack behaviours, including source distribution, traffic volume, infrastructure targeting, and financial exploitation. These conditions enable consistent classification of known attacks-such as DoS, DDoS, LDoS, LDDoS, EDoS, DoW, and DDoW, while supporting identification of emerging or hybrid variants. The lattice structure captures the cumulative satisfaction of conditions, allowing hierarchical reasoning across denial attack classes. The Venn diagram highlights conceptual overlaps between availability- and sustainability-focused attacks, improving comparative insight. Together, these models provide a robust analytical lens for threat modeling, mitigation strategy design, and attacker intent classification. The framework is particularly relevant in cloud-native and serverless environments, where sustainability-based attacks are increasingly impactful yet under-recognised. Its extensibility also permits future integration of socio-technical or behavioural dimensions. By offering a structured taxonomy with theoretical grounding and real-world applicability, this work advances denial attack comprehension and equips defenders, researchers, and cloud architects with a shared vocabulary for interpreting and mitigating evolving threat vectors.
Large VLM-based Stylized Sports Captioning
Venue: arXiv cs.CV 2025
Year: 2025

The advent of large (visual) language models (LLM / LVLM) have led to a deluge of automated human-like systems in several domains including social media content generation, search and recommendation, healthcare prognosis, AI assistants for cognitive tasks etc. Although these systems have been successfully integrated in production; very little focus has been placed on sports, particularly accurate identification and natural language description of the game play. Most existing LLM/LVLMs can explain generic sports activities, but lack sufficient domain-centric sports' jargon to create natural (human-like) descriptions. This work highlights the limitations of existing SoTA LLM/LVLMs for generating production-grade sports captions from images in a desired stylized format, and proposes a two-level fine-tuned LVLM pipeline to address that. The proposed pipeline yields an improvement > 8-10% in the F1, and > 2-10% in BERT score compared to alternative approaches. In addition, it has a small runtime memory footprint and fast execution time. During Super Bowl LIX the pipeline proved its practical application for live professional sports journalism; generating highly accurate and stylized captions at the rate of 6 images per 3-5 seconds for over 1000 images during the game play.
A goal-driven ruin and recreate heuristic for the 2D variable-sized bin packing problem with guillotine constraints
Venue: arXiv cs.CG 2025
Year: 2025

This paper addresses the two-dimensional bin packing problem with guillotine constraints. The problem requires a set of rectangular items to be cut from larger rectangles, known as bins, while only making use of edge-to-edge (guillotine) cuts. The goal is to minimize the total bin area needed to cut all required items. This paper also addresses variants of the problem which permit 90° rotation of items and/or a heterogeneous set of bins. A novel heuristic is introduced which is based on the ruin and recreate paradigm combined with a goal-driven approach. When applying the proposed heuristic to benchmark instances from the literature, it outperforms the current state-of-the-art algorithms in terms of solution quality for all variants of the problem considered.